Leveraging Deep Learning for Enhanced Information Security: A Comprehensive Approach to Threat Detection and Mitigation

Forcing developments in cyberspace means protecting information resources requires enhanced and more dynamic protection models. Traditional approaches don’t adequately address the numerous, sophisticated, varied, and frequently intersecting emergent security challenges, such as malware, phishing, and DDoS attacks. This paper introduces a novel hybrid deep learning framework leveraging convolutional neural networks (CNN) and recurrent neural networks (RNN) for enhanced threat detection and mitigation within a Zero Trust Architecture (ZTA). The model identifies anomalies indicative of potential security threats by analysing large network traffic datasets. To decrease false positive instances, autoencoders are integrated, significantly improving the system’s ability to differentiate between normal and anomalous behaviour. Extensive experiments were conducted using a benchmark cybersecurity dataset, achieving an accuracy rate of 98.75% and a false positive rate of only 1.43%. Compared to traditional approaches, this dynamic deep learning framework is highly adaptable, requiring little oversight to respond effectively to new and evolving threats. From the study results, it can be concluded that deep learning provides a robust and scalable solution for addressing emerging cyber threats and creating a more secure and reliable information security environment. Future work will focus on extending the framework to improve its accuracy and robustness, further advancing cybersecurity capabilities. This research significantly contributes to information security, establishing a promising direction for applying machine learning to enhance cybersecurity.

@article{Wang2024,
  title     = {Leveraging Deep Learning for Enhanced Information Security: A Comprehensive Approach to Threat Detection and Mitigation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {KaiJing Wang},
  doi       = {10.14569/IJACSA.2024.0151296},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151296}
}